Update app.py

app.py

@@ -1,54 +1,86 @@
 import streamlit as st
-import requests
+import yfinance as yf
 import pandas as pd
+from thronetrader import StrategicSignals
+from Pandas_Market_Predictor import Pandas_Market_Predictor
+
+
 
 # Hard-coded API key for demonstration purposes
 API_KEY = "QR8F9B7T6R2SWTAT"
 
 def fetch_alpha_vantage_data(api_key, symbol):
-    try:
-        url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=5min&apikey={api_key}'
-        response = requests.get(url)
-        response.raise_for_status()  # Raise an error for bad responses
-        alpha_vantage_data = response.json()
-        return alpha_vantage_data
-    except requests.RequestException as e:
-        st.error(f"Error fetching data: {e}")
-        return None
+    
+    url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=5min&apikey={api_key}'
+    response = requests.get(url)
+    alpha_vantage_data = response.json()
+    return alpha_vantage_data
 
-def main():
-    st.title("Stock Data Analysis")
+def calculate_indicators(data):
+    # Convert all columns to numeric
+    data = data.apply(pd.to_numeric, errors='coerce')
+
+    # Example: Simple condition for doji and inside
+    data['Doji'] = abs(data['Close'] - data['Open']) <= 0.01 * (data['High'] - data['Low'])
+    data['Inside'] = (data['High'] < data['High'].shift(1)) & (data['Low'] > data['Low'].shift(1))
+    return data
 
-    # User input for stock symbol
-    symbol = st.text_input("Enter Stock Symbol (e.g., IBM):")
+def display_signals(signal_type, signals):
+    st.subheader(f"{signal_type} Signals:")
+    st.write(signals)
 
-    if not symbol:
-        st.warning("Please enter a valid stock symbol.")
-        st.stop()
+def main():
+    st.title("Stock Trend Predictor")
 
-    # Use the hard-coded API key
-    api_key = API_KEY
+    # Input for stock symbol
+    symbol = st.text_input("Enter stock symbol (e.g., AAPL):", "AAPL")
 
     # Fetch Alpha Vantage data
-    alpha_vantage_data = fetch_alpha_vantage_data(api_key, symbol)
-
-    if alpha_vantage_data:
-        # Extract relevant data from Alpha Vantage response
-        time_series_key = 'Time Series (5min)'
-        if time_series_key in alpha_vantage_data:
-            alpha_vantage_time_series = alpha_vantage_data[time_series_key]
-            df = pd.DataFrame(alpha_vantage_time_series).T
-            df.index = pd.to_datetime(df.index)
-            df = df.dropna(axis=0)
-
-            if not df.empty:
-                # Display the raw data
-                st.subheader("Raw Data:")
-                st.write(df)
-            else:
-                st.warning("No intraday data available for the specified symbol.")
-        else:
-            st.warning(f"Key '{time_series_key}' not found in API response.")
+    alpha_vantage_data = fetch_alpha_vantage_data(API_KEY, symbol)
+
+    # Extract relevant data from Alpha Vantage response
+    alpha_vantage_time_series = alpha_vantage_data.get('Time Series (5min)', {})
+    df = pd.DataFrame(alpha_vantage_time_series).T
+    df.index = pd.to_datetime(df.index)
+    df = df.dropna(axis=0)
+
+    # Rename columns
+    df = df.rename(columns={'1. open': 'Open', '2. high': 'High', '3. low': 'Low', '4. close': 'Close', '5. volume': 'Volume'})
+
+    # Calculate indicators
+    df = calculate_indicators(df)
+
+    # Display stock trading signals
+    strategic_signals = StrategicSignals(symbol=symbol)
+
+    # Display loading message during processing
+    with st.spinner("Predicting signals using Strategic Indicators..."):
+        # Display signals
+        st.subheader(":orange[Strategic Indicators Trend Prediction]")
+        display_signals("Bollinger Bands", strategic_signals.get_bollinger_bands_signals())
+        display_signals("Breakout", strategic_signals.get_breakout_signals())
+        display_signals("Crossover", strategic_signals.get_crossover_signals())
+        display_signals("MACD", strategic_signals.get_macd_signals())
+        display_signals("RSI", strategic_signals.get_rsi_signals())
+
+    # Create predictor
+    my_market_predictor = Pandas_Market_Predictor(df)
+
+    # Predict Trend
+    indicators = ["Doji", "Inside"]
+
+    # Display loading message during prediction
+    with st.spinner("Predicting trend using AI ...."):
+        # Predict trend
+        trend = my_market_predictor.Trend_Detection(indicators, 10)
+
+    # Display results
+    st.subheader(":orange[AI Trend Prediction]")
+    st.write("Buy Trend :", trend['BUY'])
+    st.write("Sell Trend :", trend['SELL'])
+
+    # Delete the DataFrame to release memory
+    del df
 
 if __name__ == "__main__":
     main()